Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

Wang, Yuting; Wang, Shujian; Xu, Ming

doi:10.3390/ijerph19020629

Cited by 12 publications

(6 citation statements)

References 54 publications

(58 reference statements)

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“…The accuracy of GA-BP network is 90.8%, which is better than traditional BP network and support vector machine [33]. In 2022, Wang Yuting compared the classification results of EEG signals by back propagation (BP) neural network, K-nearest neighbour classification (KNN), Random forest (RF) and support vector machine (SVM), and concluded that the SVM had the best classification effect [34][35][36][37][38]. In 2022, Zeng Wei et al proposed a laser ultrasound detection method based on the PSO-SVM algorithm for the identification and detection of human skin tumours [39].…”

Section: Research On Physiological Signal Processing Methodsmentioning

confidence: 99%

Research on the evaluation of rectal function after LAR based on CEEMDAN‐Fast‐ICA algorithm

Zan

Zhao

Zhong

et al. 2023

IET Science Measure & Tech

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Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi-sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one-dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast-ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO-SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.

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Section: Research On Physiological Signal Processing Methodsmentioning

confidence: 99%

Research on the evaluation of rectal function after LAR based on CEEMDAN‐Fast‐ICA algorithm

Zan

Zhao

Zhong

et al. 2023

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…Each individual decision tree generates a prediction result, and the final prediction is performed by implementing the major voting method, as shown in Figure 4 . The RF classifier has been widely utilized to classify EEG signals for evaluating the stages of sleep and diagnosing sleep problems [ 20 ] and identifying landscape perception [ 21 ] due to the good balance between execution time and reliability. Therefore, RF is employed to classify the EEG signals for the evaluation of the performance of the features extracted from the AE.…”

Section: Methodsmentioning

confidence: 99%

A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal

Huang

Sun

Zhang

et al. 2022

IJERPH

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Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses. The lack of model interpretability also impedes the wider application and deeper understanding of ML methods in epilepsy detection. In this research, a novel feature extraction method based on an autoencoder (AE) is proposed in the time domain. The architecture and mechanism are elaborated. In this method, specified features are defined and calculated on the basis of signal reconstruction quantification of the AE. The EEG recognition is performed to validate the effectiveness of the proposed detection method, and the prediction accuracy reached 97%. To further investigate the superiority of the proposed AE-based feature extraction method, a widely used feature extraction method, PCA, is allocated for comparison. In order to understand the underlying working mechanism, permutation importance and SHapley Additive exPlanations (SHAP) are conducted for model interpretability, and the results further confirm the reasonability and effectiveness of the extracted features by AE reconstruction. With high computing efficiency in the time domain and an extensively satisfactory accuracy, the proposed epilepsy detection method exhibits great superiority and potential in almost real-time and automatic epilepsy monitoring.

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“…To evaluate the classification performance, this study compared the experimental results of different methods: naive bayes [2], support vector machine (SVM) [27],random forest [50], deep recurrent-CNN for EEG images [51] (referred to as iDRCNN in the remainder of this manuscript), EEGNet [30], DeepConvNet [9], ShallowConvNet [9], and Conformer [18].…”

Section: Model Trainingmentioning

confidence: 99%

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

Liang,

Zhang,

et al. 2024

J. Neural Eng.

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Objective Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Results In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Finally, we perform visualization of the channel-attention to project the model's reliance on different channels onto scalp topographic maps. Significance FetchEEG is a novel hybrid method based on EEG for emotion classification, which significantly improves the performance of recognition and is effective and feasible.

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Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

Cited by 12 publications

References 54 publications

Research on the evaluation of rectal function after LAR based on CEEMDAN‐Fast‐ICA algorithm

Research on the evaluation of rectal function after LAR based on CEEMDAN‐Fast‐ICA algorithm

A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

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